Study examines potential use of machine learning for sustainable
development of biomass

Date:
March 7, 2023
Source:
Yale University
Summary:
Machine learning can be valuable in supporting sustainable
development of biomass if it is applied across the entire lifecyle
of biomass and biomass-derived products, according to a new study.


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FULL STORY ========================================================================== Biomass is widely considered a renewable alternative to fossil fuels,
and many experts say it can play a critical role in combating climate
change. Biomass stores carbon and can be turned into bio-based products
and energy that can be used to improve soil, treat wastewater, and
produce renewable feedstock.


==========================================================================
Yet large-scale production of it has been limited due to economic
constraints and challenges to optimizing and controlling biomass
conversion.

A new study led by Yale School of the Environment's Yuan Yao,
assistant professor of industrial ecology and sustainable systems, and
doctoral student Hannah Szu-Han Wang, analyzed current machine learning applications for biomass and biomass-derived materials (BDM) to determine
if machine learning is advancing the research and development of biomass products. The study authors found that machine learning has not been
applied across the entire life cycle of BDM, limiting its ability for development.

Yao's research investigates how emerging technologies and industrial development will affect the environment with a focus on bioeconomy and sustainable production. Wang worked in the production of biomaterials
during her master's research. The two researchers said they were
interested in pursuing this study to find out if machine learning
could help with best practices for creating BDM, a chief component of a bio-based economy, as well as predicting their performance as sustainable materials.

"There are so many combinations of biomass feedstock, conversion
technologies, and BDM applications. If we want to try each combination
using the traditional trial-and-error experimental approach, this will
take a lot of time, labor, effort, and energy. We already generate a
lot of data from these past experiments, so we are asking, can we apply
machine learning to help us to figure out how we can better design
BDM?" Yao explains.

For the study, which was published in Resources, Conservation and
Recycling, Yao and Wang reviewed more than 50 papers published since
2008 to understand the capabilities, current limitations, and future
potential of machine learning in supporting sustainable development and applications of BDM. What they found is that while a few studies applied machine learning to address data challenges for life cycle assessment,
most studies only applied machine learning to predict and optimize the technical performance of biomass conversion and applications. None
reviewed machine learning applications across the entire lifecycle,
from biomass cultivation to BDM production and end-use applications.

"Most studies are applying machine learning to just a very small part
of the entire lifecycle of BDM," Yao says. "Our argument is that if
you really want to incorporate sustainability into development of this material, we need to consider the entire lifecycle of the materials, from
how they are generated to their potential environmental impact. We believe machine learning has the potential to support sustainability-informed
design for biomass-derived materials." Wang said the study has led to
further research on data gaps in machine learning on biomass-derived
materials.

"We found a future direction that people have not yet explored in terms
of sustainability assessments for BDM. There needs to be a full pathway prediction to enhance our understanding of how various factors regarding
BDM interact and contribute to sustainability," she says.

* RELATED_TOPICS
o Plants_&_Animals
# Ecology_Research # Agriculture_and_Food # Soil_Types #
Animal_Learning_and_Intelligence
o Earth_&_Climate
# Sustainability # Environmental_Awareness #
Energy_and_the_Environment # Environmental_Issues
* RELATED_TERMS
o Biomass o Overfishing o Hydrogen_vehicle o
Smoulder o Biomass_(ecology) o Renewable_energy o
Computational_neuroscience o Carbon_dioxide_sink

========================================================================== Story Source: Materials provided by Yale_University. Note: Content may
be edited for style and length.


========================================================================== Journal Reference:
1. Hannah Szu-Han Wang, Yuan Yao. Machine learning for sustainable
development and applications of biomass and biomass-derived
carbonaceous materials in water and agricultural systems: A
review. Resources, Conservation and Recycling, 2023; 190: 106847
DOI: 10.1016/ j.resconrec.2022.106847 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/03/230307174307.htm

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